A highly sensitive fiber optic refractive index (RI) sensor with low temperature cross-sensitivity is proposed and demonstrated. The RI sensitivity of Mach-Zehnder interferometer (MZI), which generated by the optical path difference between HE ₁₁ and HE ₁₂ modes in tapered microfiber, can be theoretically improved to ±∞ by operating at the dispersion turning point (DTP), but limited by the narrow free spectral range (FSR) and fragile sensor structure as the waist diameter is less than 3 um at DTP. To solve the problem of fragile sensor structure and difficulty in micromachining of traditional tapered microfiber working at the DTP, Vernier effect is introduced by cascading two microfiber Mach-Zehnder interferometers (MZIs) with larger diameters of about 8 um. The RI sensitivity can be amplified 20 times reaching 46467 nm/RIU with temperature cross-sensitivity only -1.21×10 ^-4 RIU/°C in aqueous solution. Moreover, the magnification factor M can be adjusted desirably as the RI sensitivity and FSR of single MZI can be adjusted by changing the waist diameter and the waist length, then the RI sensitivity can be improved further exceeding 10 ⁶ nm/RIU, which is the highest of all the fiber optic RI sensors to the best of our knowledge. The proposed RI sensor with ultrahigh sensitivity, robust and reliable structure shows great significance for biochemical trace detection, such as slight RI variations caused by small biological molecules or extremely low concentration biological samples.

A simple Mach-Zehnder interferometer (MZI) induced by no-core-fiber (NCF) offset microcavity is proposed and demonstrated for temperature self-compensated biosensing. The microcavity is fabricated by on-line fiber cutting-welding method. Benefiting from the directly interact between microfluidics and fiber core signal in microcavity instead of evanescent wave, an ultrahigh RI sensitivity of -12146 nm/RIU was obtained by experiment with temperature cross-sensitivity of 2.05 nm/°C. The influence of microcavity length was analyzed in detail. Temperature self-compensation can be realized by solving a multivariate matrix. Finally, the bovine serum albumin (BSA) samples in different concentrations were detected to evaluate the biosensing capability. Experiments demonstrated that the designed sensor exhibited a BSA concentration sensitivity of -10.4 nm/mg/mL and a detection limit of 1.9 μg/mL, which shows great potential for label-free biochemical trace detections without temperature-induced cross-sensitivity.

Objective: Heart disease is a leading cause of death and disability among middle-aged and elderly populations. Depression is a common comorbidity that impairs prognosis and quality of life. This study aimed to develop a machine learning (ML)-based depression risk prediction model based on China Health and Retirement Longitudinal Study (CHARLS) data. Methods: A total of 947 middle-aged and elderly heart disease patients from CHARLS 2015 were included after applying missing data criteria. Missing values were filled using random forest (RF), and data were split 7:3 into training and validation cohorts. Variables selection in the training cohort using univariate analysis, Lasso regression, recursive feature elimination (RFE), and feature importance evaluation using RF and decision tree (DT). Variables appearing in at least three of these five methods were selected. Eleven ML models were constructed and evaluated by area under the curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, F1 score, calibration curve and decision curve analysis. Five-fold cross-validation enhanced stability and SHapley Additive exPlanation (SHAP) values interpreted feature importance. Results: Fifty-eight variables were extracted. After multi-step variable selection within the training cohort, nine variables (address, grip-max, arthritis rheumatism, Hope, sleep time, pain, Retire, ADL, IADL) were initially identified. Among 11 ML models, the logistic regression (LR) algorithm demonstrated the best overall performance with an AUC of 0.792 in the validation cohort. A 4-variable LR model (pain, address, sleep time, and grip-max) was optimized, achieving a comparable AUC of 0.788. SHAP analysis confirmed pain as the most critical predictor (69.0% of depressed patients reported pain versus 26.9% of non-depressed patients). Rural residence (86.5% vs. 66.7%), shorter sleep time (median 5.25(4.00, 7.00) vs. 6.00(5.00, 8.00) hours), and lower grip-max (24.50(20.00, 30.00) vs. 27.00(22.50, 33.40) increased depression risk. A user-friendly web-based calculator was developed for clinical applications. Conclusions: The simplified LR model exhibits robust predictive performance and clinical applicability for assessing high depression risk in middle-aged and elderly patients with heart disease.